
French startup FlexAI exits stealth with $30M to ease access to AI compute tcrn.ch/3JzhZty
Dali Kilani
4.6K posts

@dadicool
CTO and serial Entrepreneur, x-FlexAI, x-Lifen, x-BCG,x-Zynga, x-Nvidia,x-Ciena Passionate Tunisian about Cloud Infrastructure, Healthcare, Security, AI

French startup FlexAI exits stealth with $30M to ease access to AI compute tcrn.ch/3JzhZty



A mathematician who shared an office with Claude Shannon at Bell Labs gave one lecture in 1986 that explains why some people win Nobel Prizes and other equally smart people spend their whole lives doing forgettable work. His name was Richard Hamming. He won the Turing Award. He invented error-correcting codes that made modern computing possible. And he spent 30 years at Bell Labs sitting in a cafeteria at lunch watching which scientists became legendary and which ones faded into nothing. In March 1986, he walked into a Bellcore auditorium in front of 200 researchers and told them exactly what he had seen. Here's the framework that has been quoted by every serious scientist for the last 40 years. His opening line landed like a punch. He said most scientists he worked with at Bell Labs were just as smart as the Nobel Prize winners. Just as hardworking. Just as credentialed. And yet at the end of a 40-year career, one group had changed entire fields and the other group was forgotten by the time they retired. He wanted to know what the difference actually was. And he said it wasn't luck. It wasn't IQ. It was a specific set of habits that almost nobody is willing to follow. The first habit was the one that hurts the most to hear. He said most scientists deliberately avoid the most important problem in their field because the odds of failure are too high. They pick a safe adjacent problem, solve it cleanly, publish it, and move on. And because they never swing at the hard problem, they never hit it. He said if you do not work on an important problem, it is unlikely you will do important work. That is not a motivational line. That is a logical one. The second habit was about doors. Literal doors. He noticed that the scientists at Bell Labs who kept their office doors closed got more done in the short term because they had no interruptions. But the scientists who kept their doors open got more done over a career. The open-door scientists were interrupted constantly. They also absorbed every new idea passing through the hallway. Ten years in, they were working on problems the closed-door scientists did not even know existed. The third habit was inversion. When Bell Labs refused to give him the team of programmers he wanted, Hamming sat with the rejection for weeks. Then he flipped the question. Instead of asking for programmers to write the programs, he asked why machines could not write the programs themselves. That single inversion pushed him into the frontier of computer science. He said the pattern repeats everywhere. What looks like a defect, if you flip it correctly, becomes the exact thing that pushes you ahead of everyone else. The fourth habit was the one that hit me the hardest. He said knowledge and productivity compound like interest. Someone who works 10 percent harder than you does not produce 10 percent more over a career. They produce twice as much. The gap doesn't add. It multiplies. And it compounds silently for years before anyone notices. He finished the lecture with a line I have never been able to shake. He said Pasteur's famous quote is right. Luck favors the prepared mind. But he meant it literally. You don't hope for luck. You engineer the conditions where luck can land on you. Open doors. Important problems. Inverted questions. Compounded hours. Those are not traits. Those are choices you make every single day. The transcript has been sitting on the University of Virginia's computer science website for almost 30 years. The video is free on YouTube. Stripe Press reprinted the full lectures as a book in 2020 and Bret Victor wrote the foreword. Hamming died in 1998. He gave his final lecture a few weeks before. He was 82. The lecture that explains why some careers become legendary and others disappear is still free. Most people who could benefit from it will never open it.










Hot Take: Anthropic will acquire Linear. Coding is solved post-Opus 4.5. All the devs building CLIs and IDEs are working at the wrong level of abstraction. Why? The current bottlenecks in Agents are: - PR Reviews (h/t @rfgarcia). For side projects, Greptile can autonomously handle PRs (@Steve_Yegge didn't write a single line of code for Beads, 140k LOC). For prod deployments, the CTO still needs to have an understanding of what is happening, and to socialize those learnings organization-wide for cross-functional product roadmapping. - Agent Orchestration - This is being solved by Agent Cloud Mail by @doodlestein (built on Beads) , @TaskmasterAI by @EyalToledano. @intelligenceco is trying to solve this in the general agent scenario, but it will be solved in coding first. - Memory, but not an issue in coding with task decomposition and graph-based dependency planning a la Beads. The future will look like Technical PMs/Designers, CEOs, CTOs, and Staff Engineers working out of Linear/Asana (the company coordination layer), directing Swarms of Coding Agents, occasionally dropping into Cursor/CC for deeper understanding and debugging. Generation is solved. The frontier is Orchestration - the logical next step on Karpathy's "autonomy slider". 3-6 months.

I stole this idea and now use it with every single employee. It’s the best illustration I’ve seen of teaching someone to be high agency. It says there are 5 levels of work: Level 1: “There is a problem.” Level 2: “There is a problem, and I’ve found some causes.” Level 3: “Here’s the problem, here are some possible causes, and here are some possible solutions.” Level 4: “Here’s the problem, here’s what I think caused it, here are some possible solutions, and here’s the one I think we should pick.” Level 5: “I identified a problem, figured out what caused it, researched how to fix it, and I fixed it. Just wanted to keep you in the loop.” Using this framework, here’s what I say to every new employee… You will live at Level 4 from Day 1 and as we build trust you will rise to Level 5. Being high agency doesn’t just mean tackling problems in this way. It means your entire way of working should be oriented to being a Level 4+ employee. Plz feel free to steal it as well. And ty @stephsmithio for the framework!



It's not about GPUs anymore; it's all about POWER. A comment from a $GOOGL employee working on datacenters: Getting GPUs and TPUs is not a bottleneck. "Power, lack of available power, reliable power has become the biggest bottleneck for us." on @AlphaSenseInc


The longer you spend in tech, the stronger the urge to buy a farm and never touch a computer again in your life.

.@pmarca: "The person who writes down the thing has tremendous power." In most companies, almost no one does it. If you can turn chaos into a coherent plan on paper, people will follow your lead, whether you have the title or not.

0/ Autonomous agents are about to become Ethereum’s biggest power users. Guest thread from @kleffew94 and @MurrLincoln on how a long-forgotten HTTP status code, ‘402 Payment Required’ could unlock a new frontier for Ethereum: agentic commerce. 🧵



1/N I’m excited to share that our latest @OpenAI experimental reasoning LLM has achieved a longstanding grand challenge in AI: gold medal-level performance on the world’s most prestigious math competition—the International Math Olympiad (IMO).



Seven facts about Operation “Spiderweb” — a Ukrainian strike that will go down in history as one of the most successful special operations ever conducted. 1. Ukrainian special forces spent 1.5 years preparing and planning the attack. 1/n